摘要
The study was to compare principle component (PC) versus partial least square (PLS) regression, the former unsupervised and the latter supervised gene component analysis, for highly complicated and correlated microarray gene expression profile. Projection of derived classifiers into independent samples for clinical phenotype prediction was evaluated as well. Previous studies had suggested that PLS might be superior to PC regression in the task of tumor classification since the covariance between predictive and respondent variables was maximized for latent factor extraction. We applied both algorithms for classifier construction and validated their prediction performance on independent microarray experiments. The statistical strategy could reduce high-dimensionality of microarray features and avoid the collinearity problem inherited in gene expression profiles. Proposed predictive model could discriminate breast cancers with positive and negative estrogen receptor status successfully and was feasible for both Taiwanese and Chinese females, both with the same Han Chinese ethnic origin.
| 原文 | 英語 |
|---|---|
| 主出版物標題 | 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010 |
| 頁面 | 506-511 |
| 頁數 | 6 |
| DOIs | |
| 出版狀態 | 已發佈 - 2010 |
| 事件 | 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010 - HongKong, 中国 持續時間: 十二月 18 2010 → 十二月 21 2010 |
其他
| 其他 | 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010 |
|---|---|
| 國家 | 中国 |
| 城市 | HongKong |
| 期間 | 12/18/10 → 12/21/10 |
指紋
ASJC Scopus subject areas
- Biomedical Engineering
- Health Informatics
引用此文
Projecting partial least square and principle component regression across microarray studies. / Hunag, Chi Cheng; Tu, Shin Hsin; Lien, Heng Hui; Huang, Ching Shui; Chuang, Eric Y.; Lai, Liang Chuan.
2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010. 2010. p. 506-511 5703853.研究成果: 書貢獻/報告類型 › 會議貢獻
}
TY - GEN
T1 - Projecting partial least square and principle component regression across microarray studies
AU - Hunag, Chi Cheng
AU - Tu, Shin Hsin
AU - Lien, Heng Hui
AU - Huang, Ching Shui
AU - Chuang, Eric Y.
AU - Lai, Liang Chuan
PY - 2010
Y1 - 2010
N2 - The study was to compare principle component (PC) versus partial least square (PLS) regression, the former unsupervised and the latter supervised gene component analysis, for highly complicated and correlated microarray gene expression profile. Projection of derived classifiers into independent samples for clinical phenotype prediction was evaluated as well. Previous studies had suggested that PLS might be superior to PC regression in the task of tumor classification since the covariance between predictive and respondent variables was maximized for latent factor extraction. We applied both algorithms for classifier construction and validated their prediction performance on independent microarray experiments. The statistical strategy could reduce high-dimensionality of microarray features and avoid the collinearity problem inherited in gene expression profiles. Proposed predictive model could discriminate breast cancers with positive and negative estrogen receptor status successfully and was feasible for both Taiwanese and Chinese females, both with the same Han Chinese ethnic origin.
AB - The study was to compare principle component (PC) versus partial least square (PLS) regression, the former unsupervised and the latter supervised gene component analysis, for highly complicated and correlated microarray gene expression profile. Projection of derived classifiers into independent samples for clinical phenotype prediction was evaluated as well. Previous studies had suggested that PLS might be superior to PC regression in the task of tumor classification since the covariance between predictive and respondent variables was maximized for latent factor extraction. We applied both algorithms for classifier construction and validated their prediction performance on independent microarray experiments. The statistical strategy could reduce high-dimensionality of microarray features and avoid the collinearity problem inherited in gene expression profiles. Proposed predictive model could discriminate breast cancers with positive and negative estrogen receptor status successfully and was feasible for both Taiwanese and Chinese females, both with the same Han Chinese ethnic origin.
UR - http://www.scopus.com/inward/record.url?scp=79952028829&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79952028829&partnerID=8YFLogxK
U2 - 10.1109/BIBMW.2010.5703853
DO - 10.1109/BIBMW.2010.5703853
M3 - Conference contribution
AN - SCOPUS:79952028829
SN - 9781424483044
SP - 506
EP - 511
BT - 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010
ER -